Geocoding Quality and Spatial Analysis with Street Reference Data

Accurate spatial analysis relies heavily on the quality of your underlying geographic data. In urban planning and GIS projects, understanding how street reference data impacts geocoding precision is critical for making informed, data-driven decisions. This course guides you from foundational spatial concepts to evaluating and improving the accuracy of your geographic coordinates. You will transition from a beginner to a confident practitioner capable of assessing spatial data quality, identifying geocoding errors, and selecting the right reference datasets for your projects. What you'll learn: - Understand the core principles of geocoding, address matching, and coordinate systems - Evaluate how different street reference datasets affect the precision of spatial analysis - Identify and resolve common geocoding errors, interpolation issues, and positional inaccuracies - Compare open-source and proprietary reference data sources for urban planning applications - Apply modern data quality metrics to assess the reliability of your geographic outputs - Practice structuring address data to optimize matching rates and spatial accuracy This course begins with essential terminology, basic spatial concepts, and the mechanics of address matching. You will then explore practical methodologies to analyze reference data quality, test for positional accuracy, and apply these techniques to real-world urban planning scenarios. Written with clarity and focus, the text guides you step-by-step through database preparation and spatial evaluation workflows. This course is designed for beginners in GIS, urban planning students, and data analysts who want to ensure the integrity of their spatial projects. No prior geocoding experience is required. Start mastering the fundamentals of spatial data quality and elevate the accuracy of your geographic analysis today.